GIS layers
@rvosa
2023-05-23
Introduction
GIS data releases such as BioClim (WorldClim) and ENVIREM are very useful for eco-evolutionary research. However, some of the layers are hard to develop an intuition for based solely on the definition of what the variable means. Here we provide worldwide maps of the variables. The main purpose of this is to be of help in interpreting the results of ENM/SDM analyses or other approaches that select variables out of larger sets, for example when including OMI- transformed point estimates of species optima. We therefore map these data only at the lowest resolution (10 arc minutes) to get a global perspective without too much data.
ENVIREM
Title P.O., Bemmels J.B. 2018. ENVIREM: an expanded set of bioclimatic and topographic variables increases flexibility and improves performance of ecological niche modeling. Ecography. 41:291–307. doi:10.1111/ecog.02880
Data at: https://envirem.github.io/
Environmental variables that are thought to be relevant to species’ ecology and geographic distribution are essential for applications such as species distribution modeling. However, the number of such variables that are available, that span multiple time periods, and that can easily be integrated with other datasets is very limited.
With the ENVIREM dataset, we provide a number of climatic and topographic variables that have been described in the literature, and make them available at the same resolutions as are available at WorldClim, and for both current and past time periods.
annualPET
Annual potential evapotranspiration: a measure of the ability of the atmosphere to remove water through evapotranspiration processes, given unlimited moisture.
This is high in regions with high actual or possible evaporation. Not necessarily tracking the latitudinal bands precisely: areas with high values include the Taklamakan desert, and Southern Iberia, for example.
This measure expresses the potential, so it is unaffected by actual availability or lack of water. Which means there is little difference between, for example, the dry and wet parts of Africa.
GDALinfo("../data/envirem/1.0/10m/current_10arcmin_annualPET.tif")## Warning in getProjectionRef(x, OVERRIDE_PROJ_DATUM_WITH_TOWGS84 = OVERRIDE_PROJ_DATUM_WITH_TOWGS84, : Discarded datum unknown in Proj4 definition: +proj=longlat +ellps=WGS84 +towgs84=0,0,0,0,0,0,0 +no_defs,
## but +towgs84= values preserved## rows 900
## columns 2160
## bands 1
## lower left origin.x -180
## lower left origin.y -60
## res.x 0.1666667
## res.y 0.1666667
## ysign -1
## oblique.x 0
## oblique.y 0
## driver GTiff
## projection +proj=longlat +ellps=WGS84 +towgs84=0,0,0,0,0,0,0 +no_defs
## file ../data/envirem/1.0/10m/current_10arcmin_annualPET.tif
## apparent band summary:
## GDType hasNoDataValue NoDataValue blockSize1 blockSize2
## 1 Float32 TRUE -9999 1 2160
## apparent band statistics:
## Bmin Bmax Bmean Bsd
## 1 49.33 2351.07 1035.764 611.5069
## Metadata:
## AREA_OR_POINT=Arear <- raster("../data/envirem/1.0/10m/current_10arcmin_annualPET.tif", values=T, nrows=900, ncols=2160, xmn=-180, xmx=180, ymn=-60, ymx=60)
plot(r)
aridityIndexThornthwaite
Thornthwaite aridity index: Index of the degree of water deficit below water need. This measure is strongly influenced by how much water is available. Values are high in areas that are actually dry (Sahara) but low in areas with a lot of rain (e.g. Congo basin).
GDALinfo("../data/envirem/1.0/10m/current_10arcmin_aridityIndexThornthwaite.tif")## Warning in getProjectionRef(x, OVERRIDE_PROJ_DATUM_WITH_TOWGS84 = OVERRIDE_PROJ_DATUM_WITH_TOWGS84, : Discarded datum unknown in Proj4 definition: +proj=longlat +ellps=WGS84 +towgs84=0,0,0,0,0,0,0 +no_defs,
## but +towgs84= values preserved## rows 900
## columns 2160
## bands 1
## lower left origin.x -180
## lower left origin.y -60
## res.x 0.1666667
## res.y 0.1666667
## ysign -1
## oblique.x 0
## oblique.y 0
## driver GTiff
## projection +proj=longlat +ellps=WGS84 +towgs84=0,0,0,0,0,0,0 +no_defs
## file ../data/envirem/1.0/10m/current_10arcmin_aridityIndexThornthwaite.tif
## apparent band summary:
## GDType hasNoDataValue NoDataValue blockSize1 blockSize2
## 1 Float32 TRUE -9999 1 2160
## apparent band statistics:
## Bmin Bmax Bmean Bsd
## 1 0 100 55.28139 26.44716
## Metadata:
## AREA_OR_POINT=Arear <- raster("../data/envirem/1.0/10m/current_10arcmin_aridityIndexThornthwaite.tif", values=T, nrows=900, ncols=2160, xmn=-180, xmx=180, ymn=-60, ymx=60)
plot(r)
climaticMoistureIndex
A metric of relative wetness and aridity. Values are high in wet areas, such as the west coasts of continents, especially at higher latitudes, or where mountains force precipitation (e.g. Alps, Himalayas), or in the wet, equatorial tropics.
GDALinfo("../data/envirem/1.0/10m/current_10arcmin_climaticMoistureIndex.tif")## rows 900
## columns 2160
## bands 1
## lower left origin.x -180
## lower left origin.y -60
## res.x 0.1666667
## res.y 0.1666667
## ysign -1
## oblique.x 0
## oblique.y 0
## driver GTiff
## projection +proj=longlat +datum=WGS84 +no_defs
## file ../data/envirem/1.0/10m/current_10arcmin_climaticMoistureIndex.tif
## apparent band summary:
## GDType hasNoDataValue NoDataValue blockSize1 blockSize2
## 1 Float32 TRUE -9999 1 2160
## apparent band statistics:
## Bmin Bmax Bmean Bsd
## 1 -1 0.96 -0.2330531 0.4864669
## Metadata:
## AREA_OR_POINT=Arear <- raster("../data/envirem/1.0/10m/current_10arcmin_climaticMoistureIndex.tif", values=T, nrows=900, ncols=2160, xmn=-180, xmx=180, ymn=-60, ymx=60)
plot(r)
continentality
Average temp. of warmest month - average temp. of coldest month. Values are highest in the interior of large continents at high latitudes. The summers in much of Canada and in Siberia can be surprisingly warm, while the winters are very cold. Values are lowest around the equator.
GDALinfo("../data/envirem/1.0/10m/current_10arcmin_continentality.tif")## Warning in getProjectionRef(x, OVERRIDE_PROJ_DATUM_WITH_TOWGS84 = OVERRIDE_PROJ_DATUM_WITH_TOWGS84, : Discarded datum unknown in Proj4 definition: +proj=longlat +ellps=WGS84 +towgs84=0,0,0,0,0,0,0 +no_defs,
## but +towgs84= values preserved## rows 900
## columns 2160
## bands 1
## lower left origin.x -180
## lower left origin.y -60
## res.x 0.1666667
## res.y 0.1666667
## ysign -1
## oblique.x 0
## oblique.y 0
## driver GTiff
## projection +proj=longlat +ellps=WGS84 +towgs84=0,0,0,0,0,0,0 +no_defs
## file ../data/envirem/1.0/10m/current_10arcmin_continentality.tif
## apparent band summary:
## GDType hasNoDataValue NoDataValue blockSize1 blockSize2
## 1 Float32 TRUE -9999 1 2160
## apparent band statistics:
## Bmin Bmax Bmean Bsd
## 1 0.2 61.85 23.25591 14.39813
## Metadata:
## AREA_OR_POINT=Arear <- raster("../data/envirem/1.0/10m/current_10arcmin_continentality.tif", values=T, nrows=900, ncols=2160, xmn=-180, xmx=180, ymn=-60, ymx=60)
plot(r)
embergerQ
Emberger’s pluviothermic quotient: a metric that was designed to differentiate among Mediterranean type climates. At global scale, values are highest in the very wettest of the wet tropics but are lower nearly everywhere else.
GDALinfo("../data/envirem/1.0/10m/current_10arcmin_embergerQ.tif")## Warning in getProjectionRef(x, OVERRIDE_PROJ_DATUM_WITH_TOWGS84 = OVERRIDE_PROJ_DATUM_WITH_TOWGS84, : Discarded datum unknown in Proj4 definition: +proj=longlat +ellps=WGS84 +towgs84=0,0,0,0,0,0,0 +no_defs,
## but +towgs84= values preserved## rows 900
## columns 2160
## bands 1
## lower left origin.x -180
## lower left origin.y -60
## res.x 0.1666667
## res.y 0.1666667
## ysign -1
## oblique.x 0
## oblique.y 0
## driver GTiff
## projection +proj=longlat +ellps=WGS84 +towgs84=0,0,0,0,0,0,0 +no_defs
## file ../data/envirem/1.0/10m/current_10arcmin_embergerQ.tif
## apparent band summary:
## GDType hasNoDataValue NoDataValue blockSize1 blockSize2
## 1 Float32 TRUE -9999 1 2160
## apparent band statistics:
## Bmin Bmax Bmean Bsd
## 1 0 3211.74 125.6038 209.853
## Metadata:
## AREA_OR_POINT=Arear <- raster("../data/envirem/1.0/10m/current_10arcmin_embergerQ.tif", values=T, nrows=900, ncols=2160, xmn=-180, xmx=180, ymn=-60, ymx=60)
plot(r)
growingDegDays0
Sum of mean monthly temperature for months with mean temperature greater than 0℃ multiplied by number of days. Values are high in warm areas and low in cold areas. Consequently, this measure tracks latitude, except where high mountains are involved. For example, values are lower in the Andes than their latitude would suggest.
GDALinfo("../data/envirem/1.0/10m/current_10arcmin_growingDegDays0.tif")## Warning in getProjectionRef(x, OVERRIDE_PROJ_DATUM_WITH_TOWGS84 = OVERRIDE_PROJ_DATUM_WITH_TOWGS84, : Discarded datum unknown in Proj4 definition: +proj=longlat +ellps=WGS84 +towgs84=0,0,0,0,0,0,0 +no_defs,
## but +towgs84= values preserved## rows 900
## columns 2160
## bands 1
## lower left origin.x -180
## lower left origin.y -60
## res.x 0.1666667
## res.y 0.1666667
## ysign -1
## oblique.x 0
## oblique.y 0
## driver GTiff
## projection +proj=longlat +ellps=WGS84 +towgs84=0,0,0,0,0,0,0 +no_defs
## file ../data/envirem/1.0/10m/current_10arcmin_growingDegDays0.tif
## apparent band summary:
## GDType hasNoDataValue NoDataValue blockSize1 blockSize2
## 1 Float32 TRUE -9999 1 2160
## apparent band statistics:
## Bmin Bmax Bmean Bsd
## 1 0 135918 50336.51 44862.69
## Metadata:
## AREA_OR_POINT=Arear <- raster("../data/envirem/1.0/10m/current_10arcmin_growingDegDays0.tif", values=T, nrows=900, ncols=2160, xmn=-180, xmx=180, ymn=-60, ymx=60)
plot(r)
growingDegDays5
sum of mean monthly temperature for months with mean temperature greater than 5℃ multiplied by number of days. Closely tracks growingDegDays0.
GDALinfo("../data/envirem/1.0/10m/current_10arcmin_growingDegDays5.tif")## Warning in getProjectionRef(x, OVERRIDE_PROJ_DATUM_WITH_TOWGS84 = OVERRIDE_PROJ_DATUM_WITH_TOWGS84, : Discarded datum unknown in Proj4 definition: +proj=longlat +ellps=WGS84 +towgs84=0,0,0,0,0,0,0 +no_defs,
## but +towgs84= values preserved## rows 900
## columns 2160
## bands 1
## lower left origin.x -180
## lower left origin.y -60
## res.x 0.1666667
## res.y 0.1666667
## ysign -1
## oblique.x 0
## oblique.y 0
## driver GTiff
## projection +proj=longlat +ellps=WGS84 +towgs84=0,0,0,0,0,0,0 +no_defs
## file ../data/envirem/1.0/10m/current_10arcmin_growingDegDays5.tif
## apparent band summary:
## GDType hasNoDataValue NoDataValue blockSize1 blockSize2
## 1 Float32 TRUE -9999 1 2160
## apparent band statistics:
## Bmin Bmax Bmean Bsd
## 1 0 135918 48152.79 45984.14
## Metadata:
## AREA_OR_POINT=Arear <- raster("../data/envirem/1.0/10m/current_10arcmin_growingDegDays5.tif", values=T, nrows=900, ncols=2160, xmn=-180, xmx=180, ymn=-60, ymx=60)
plot(r)
maxTempColdestMonth
Max. temp. of the coldest month. Closely tracks latitude but with some mountain effects.
GDALinfo("../data/envirem/1.0/10m/current_10arcmin_maxTempColdest.tif")## Warning in getProjectionRef(x, OVERRIDE_PROJ_DATUM_WITH_TOWGS84 = OVERRIDE_PROJ_DATUM_WITH_TOWGS84, : Discarded datum unknown in Proj4 definition: +proj=longlat +ellps=WGS84 +towgs84=0,0,0,0,0,0,0 +no_defs,
## but +towgs84= values preserved## rows 900
## columns 2160
## bands 1
## lower left origin.x -180
## lower left origin.y -60
## res.x 0.1666667
## res.y 0.1666667
## ysign -1
## oblique.x 0
## oblique.y 0
## driver GTiff
## projection +proj=longlat +ellps=WGS84 +towgs84=0,0,0,0,0,0,0 +no_defs
## file ../data/envirem/1.0/10m/current_10arcmin_maxTempColdest.tif
## apparent band summary:
## GDType hasNoDataValue NoDataValue blockSize1 blockSize2
## 1 Int16 TRUE -9999 1 2160
## apparent band statistics:
## Bmin Bmax Bmean Bsd
## 1 -478 369 NaN NaN
## Metadata:
## AREA_OR_POINT=Arear <- raster("../data/envirem/1.0/10m/current_10arcmin_maxTempColdest.tif", values=T, nrows=900, ncols=2160, xmn=-180, xmx=180, ymn=-60, ymx=60)
plot(r)
minTempWarmestMonth
Min. temp. of the warmest month. Tracks latitude but with more mountain effects.
GDALinfo("../data/envirem/1.0/10m/current_10arcmin_minTempWarmest.tif")## Warning in getProjectionRef(x, OVERRIDE_PROJ_DATUM_WITH_TOWGS84 = OVERRIDE_PROJ_DATUM_WITH_TOWGS84, : Discarded datum unknown in Proj4 definition: +proj=longlat +ellps=WGS84 +towgs84=0,0,0,0,0,0,0 +no_defs,
## but +towgs84= values preserved## rows 900
## columns 2160
## bands 1
## lower left origin.x -180
## lower left origin.y -60
## res.x 0.1666667
## res.y 0.1666667
## ysign -1
## oblique.x 0
## oblique.y 0
## driver GTiff
## projection +proj=longlat +ellps=WGS84 +towgs84=0,0,0,0,0,0,0 +no_defs
## file ../data/envirem/1.0/10m/current_10arcmin_minTempWarmest.tif
## apparent band summary:
## GDType hasNoDataValue NoDataValue blockSize1 blockSize2
## 1 Int16 TRUE -9999 1 2160
## apparent band statistics:
## Bmin Bmax Bmean Bsd
## 1 -118 312 NaN NaN
## Metadata:
## AREA_OR_POINT=Arear <- raster("../data/envirem/1.0/10m/current_10arcmin_minTempWarmest.tif", values=T, nrows=900, ncols=2160, xmn=-180, xmx=180, ymn=-60, ymx=60)
plot(r)
monthCountByTemp10
Count of the number of months with mean temp greater than 10℃. Tracks latitude but with strong mountain effects. Very ‘stepped’ because values are integers (0-12) and so areas with very low and very high values could be near each other, e.g. 0 months with temp >10 high in the Andes next to areas where every month is >10 in the Amazonian rainforest.
GDALinfo("../data/envirem/1.0/10m/current_10arcmin_monthCountByTemp10.tif")## Warning in getProjectionRef(x, OVERRIDE_PROJ_DATUM_WITH_TOWGS84 = OVERRIDE_PROJ_DATUM_WITH_TOWGS84, : Discarded datum unknown in Proj4 definition: +proj=longlat +ellps=WGS84 +towgs84=0,0,0,0,0,0,0 +no_defs,
## but +towgs84= values preserved## rows 900
## columns 2160
## bands 1
## lower left origin.x -180
## lower left origin.y -60
## res.x 0.1666667
## res.y 0.1666667
## ysign -1
## oblique.x 0
## oblique.y 0
## driver GTiff
## projection +proj=longlat +ellps=WGS84 +towgs84=0,0,0,0,0,0,0 +no_defs
## file ../data/envirem/1.0/10m/current_10arcmin_monthCountByTemp10.tif
## apparent band summary:
## GDType hasNoDataValue NoDataValue blockSize1 blockSize2
## 1 Int16 TRUE -9999 1 2160
## apparent band statistics:
## Bmin Bmax Bmean Bsd
## 1 0 12 NaN NaN
## Metadata:
## AREA_OR_POINT=Arear <- raster("../data/envirem/1.0/10m/current_10arcmin_monthCountByTemp10.tif", values=T, nrows=900, ncols=2160, xmn=-180, xmx=180, ymn=-60, ymx=60)
plot(r)
PETColdestQuarter
Mean monthly PET of coldest quarter. PET = potential evapotranspiration, a measure of the ability of the atmosphere to remove water through evapotranspiration processes, given unlimited moisture. Values are highest in the semi-arid regions, such as the Sahel, next to the equatorial wet tropics.
GDALinfo("../data/envirem/1.0/10m/current_10arcmin_PETColdestQuarter.tif")## Warning in getProjectionRef(x, OVERRIDE_PROJ_DATUM_WITH_TOWGS84 = OVERRIDE_PROJ_DATUM_WITH_TOWGS84, : Discarded datum unknown in Proj4 definition: +proj=longlat +ellps=WGS84 +towgs84=0,0,0,0,0,0,0 +no_defs,
## but +towgs84= values preserved## rows 900
## columns 2160
## bands 1
## lower left origin.x -180
## lower left origin.y -60
## res.x 0.1666667
## res.y 0.1666667
## ysign -1
## oblique.x 0
## oblique.y 0
## driver GTiff
## projection +proj=longlat +ellps=WGS84 +towgs84=0,0,0,0,0,0,0 +no_defs
## file ../data/envirem/1.0/10m/current_10arcmin_PETColdestQuarter.tif
## apparent band summary:
## GDType hasNoDataValue NoDataValue blockSize1 blockSize2
## 1 Float32 TRUE -9999 1 2160
## apparent band statistics:
## Bmin Bmax Bmean Bsd
## 1 0 188.05 46.24388 51.56814
## Metadata:
## AREA_OR_POINT=Arear <- raster("../data/envirem/1.0/10m/current_10arcmin_PETColdestQuarter.tif", values=T, nrows=900, ncols=2160, xmn=-180, xmx=180, ymn=-60, ymx=60)
plot(r)
PETDriestQuarter
Mean monthly PET of driest quarter. PET = potential evapotranspiration, a measure of the ability of the atmosphere to remove water through evapotranspiration processes, given unlimited moisture. Values are highest in semi-arid zones with a dry season, for example in Mediterranean climates such as the Med itself, the US westcoast, the Cape, southern Chile and western Australia but also beyond that in the Maghreb and the Levant/Middle East.
GDALinfo("../data/envirem/1.0/10m/current_10arcmin_PETDriestQuarter.tif")## Warning in getProjectionRef(x, OVERRIDE_PROJ_DATUM_WITH_TOWGS84 = OVERRIDE_PROJ_DATUM_WITH_TOWGS84, : Discarded datum unknown in Proj4 definition: +proj=longlat +ellps=WGS84 +towgs84=0,0,0,0,0,0,0 +no_defs,
## but +towgs84= values preserved## rows 900
## columns 2160
## bands 1
## lower left origin.x -180
## lower left origin.y -60
## res.x 0.1666667
## res.y 0.1666667
## ysign -1
## oblique.x 0
## oblique.y 0
## driver GTiff
## projection +proj=longlat +ellps=WGS84 +towgs84=0,0,0,0,0,0,0 +no_defs
## file ../data/envirem/1.0/10m/current_10arcmin_PETDriestQuarter.tif
## apparent band summary:
## GDType hasNoDataValue NoDataValue blockSize1 blockSize2
## 1 Float32 TRUE -9999 1 2160
## apparent band statistics:
## Bmin Bmax Bmean Bsd
## 1 0 263.3 72.58151 68.48633
## Metadata:
## AREA_OR_POINT=Arear <- raster("../data/envirem/1.0/10m/current_10arcmin_PETDriestQuarter.tif", values=T, nrows=900, ncols=2160, xmn=-180, xmx=180, ymn=-60, ymx=60)
plot(r)
PETseasonality
Monthly variability in potential evapotranspiration. Values are highest in ‘temperate’ climates, not too hot and not too cold.
GDALinfo("../data/envirem/1.0/10m/current_10arcmin_PETseasonality.tif")## Warning in getProjectionRef(x, OVERRIDE_PROJ_DATUM_WITH_TOWGS84 = OVERRIDE_PROJ_DATUM_WITH_TOWGS84, : Discarded datum unknown in Proj4 definition: +proj=longlat +ellps=WGS84 +towgs84=0,0,0,0,0,0,0 +no_defs,
## but +towgs84= values preserved## rows 900
## columns 2160
## bands 1
## lower left origin.x -180
## lower left origin.y -60
## res.x 0.1666667
## res.y 0.1666667
## ysign -1
## oblique.x 0
## oblique.y 0
## driver GTiff
## projection +proj=longlat +ellps=WGS84 +towgs84=0,0,0,0,0,0,0 +no_defs
## file ../data/envirem/1.0/10m/current_10arcmin_PETseasonality.tif
## apparent band summary:
## GDType hasNoDataValue NoDataValue blockSize1 blockSize2
## 1 Float32 TRUE -9999 1 2160
## apparent band statistics:
## Bmin Bmax Bmean Bsd
## 1 301.19 8424.28 3925.002 1742.14
## Metadata:
## AREA_OR_POINT=Arear <- raster("../data/envirem/1.0/10m/current_10arcmin_PETseasonality.tif", values=T, nrows=900, ncols=2160, xmn=-180, xmx=180, ymn=-60, ymx=60)
plot(r)
PETWarmestQuarter
Mean monthly PET of warmest quarter. Values are highest in (semi)arid (sub)tropical areas. Especially deserts, but also steppes, prairies.
GDALinfo("../data/envirem/1.0/10m/current_10arcmin_PETWarmestQuarter.tif")## Warning in getProjectionRef(x, OVERRIDE_PROJ_DATUM_WITH_TOWGS84 = OVERRIDE_PROJ_DATUM_WITH_TOWGS84, : Discarded datum unknown in Proj4 definition: +proj=longlat +ellps=WGS84 +towgs84=0,0,0,0,0,0,0 +no_defs,
## but +towgs84= values preserved## rows 900
## columns 2160
## bands 1
## lower left origin.x -180
## lower left origin.y -60
## res.x 0.1666667
## res.y 0.1666667
## ysign -1
## oblique.x 0
## oblique.y 0
## driver GTiff
## projection +proj=longlat +ellps=WGS84 +towgs84=0,0,0,0,0,0,0 +no_defs
## file ../data/envirem/1.0/10m/current_10arcmin_PETWarmestQuarter.tif
## apparent band summary:
## GDType hasNoDataValue NoDataValue blockSize1 blockSize2
## 1 Float32 TRUE -9999 1 2160
## apparent band statistics:
## Bmin Bmax Bmean Bsd
## 1 15.67 271.7 136.1433 51.11336
## Metadata:
## AREA_OR_POINT=Arear <- raster("../data/envirem/1.0/10m/current_10arcmin_PETWarmestQuarter.tif", values=T, nrows=900, ncols=2160, xmn=-180, xmx=180, ymn=-60, ymx=60)
plot(r)
PETWettestQuarter
Mean monthly PET of wettest quarter. Values are abruptly lower in Mediterranean climate zones compared to both adjacent deserts and adjacent temperate zones.
GDALinfo("../data/envirem/1.0/10m/current_10arcmin_PETWettestQuarter.tif")## Warning in getProjectionRef(x, OVERRIDE_PROJ_DATUM_WITH_TOWGS84 = OVERRIDE_PROJ_DATUM_WITH_TOWGS84, : Discarded datum unknown in Proj4 definition: +proj=longlat +ellps=WGS84 +towgs84=0,0,0,0,0,0,0 +no_defs,
## but +towgs84= values preserved## rows 900
## columns 2160
## bands 1
## lower left origin.x -180
## lower left origin.y -60
## res.x 0.1666667
## res.y 0.1666667
## ysign -1
## oblique.x 0
## oblique.y 0
## driver GTiff
## projection +proj=longlat +ellps=WGS84 +towgs84=0,0,0,0,0,0,0 +no_defs
## file ../data/envirem/1.0/10m/current_10arcmin_PETWettestQuarter.tif
## apparent band summary:
## GDType hasNoDataValue NoDataValue blockSize1 blockSize2
## 1 Float32 TRUE -9999 1 2160
## apparent band statistics:
## Bmin Bmax Bmean Bsd
## 1 0 248.92 108.2352 51.60161
## Metadata:
## AREA_OR_POINT=Arear <- raster("../data/envirem/1.0/10m/current_10arcmin_PETWettestQuarter.tif", values=T, nrows=900, ncols=2160, xmn=-180, xmx=180, ymn=-60, ymx=60)
plot(r)
thermicityIndex
Compensated thermicity index: sum of mean annual temp., min. temp. of coldest month, max. temp. of the coldest month, x 10, with compensations for better comparability across the globe. Tracks latitude but with mountain effects.
GDALinfo("../data/envirem/1.0/10m/current_10arcmin_thermicityIndex.tif")## Warning in getProjectionRef(x, OVERRIDE_PROJ_DATUM_WITH_TOWGS84 = OVERRIDE_PROJ_DATUM_WITH_TOWGS84, : Discarded datum unknown in Proj4 definition: +proj=longlat +ellps=WGS84 +towgs84=0,0,0,0,0,0,0 +no_defs,
## but +towgs84= values preserved## rows 900
## columns 2160
## bands 1
## lower left origin.x -180
## lower left origin.y -60
## res.x 0.1666667
## res.y 0.1666667
## ysign -1
## oblique.x 0
## oblique.y 0
## driver GTiff
## projection +proj=longlat +ellps=WGS84 +towgs84=0,0,0,0,0,0,0 +no_defs
## file ../data/envirem/1.0/10m/current_10arcmin_thermicityIndex.tif
## apparent band summary:
## GDType hasNoDataValue NoDataValue blockSize1 blockSize2
## 1 Float32 TRUE -9999 1 2160
## apparent band statistics:
## Bmin Bmax Bmean Bsd
## 1 -845.75 841 158.4433 397.1017
## Metadata:
## AREA_OR_POINT=Arear <- raster("../data/envirem/1.0/10m/current_10arcmin_thermicityIndex.tif", values=T, nrows=900, ncols=2160, xmn=-180, xmx=180, ymn=-60, ymx=60)
plot(r)